Where is your POSEIDON now?

A minute of POSEIDON

• POSEIDON is an agent-based model
  • Scalable
  • Modular
• POSEIDON has a few use cases:
  • Scenario evalutation
  • Optimization
  • Reiforcement Learning

Work in progress

• Calibrating and validating West-Coast DTS model
• Develop the infrastructure for Indonesia
• Data degradation for West-Coast fixed gear model

Calibration

• We have two target data-sets:
  • Landings/quota attainments
  • Logbook data
• Calibrate in two steps
  1. Fix agents statistically
  2. Find catchabilities that generate correct quotas
  3. Fix catchabilities but let agents act adaptively
  4. Find behavioural parameters that replicate logbook
• How realistic can explore-exploit-imitate agents possibly be?

Calibration - Results

Calibration - Results 2

• Looks realistic with simple adaptive agents
• Adaptive agents are very risk-averse:
  • Calibrated exploration rate is 3.5%

Calibration - 15% exploration rate

Calibration - 3

• It’s trivial to find more performing agents:
  1. Are we picking up an approximation error from the way we distribute fish compared to the real world?
  2. Are fishers just not that profit maximizing?

Indonesia

Fixed Gear Allocation